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Estimating cotton water requirements using sentinel-2: Model development and validation
Year:
2019
Authors :
Haymann, Nitai
;
.
Kaplan, Gregoriy
;
.
Rozenstein, Offer
;
.
Tanny, Josef
;
.
Volume :
Co-Authors:
Facilitators :
From page:
485
To page:
491
(
Total pages:
7
)
Abstract:

Crop coefficient (Kc)-based estimation of crop water consumption is one of the most commonly used methods for irrigation management. In this study, cotton evapotranspiration was measured in the field during two seasons using the eddy covariance method. Kc was estimated as the ratio between reference evapotranspiration and the measured cotton evapotranspiration. In addition, a time series of Sentinel-2 imagery was processed to produce 21 vegetation indices (VIs) based on the sensor's unique spectral bands. The results suggest that most VIs that are based on Sentinel-2 bands are suitable predictors for cotton Kc, and that those based on the red and red-edge spectral bands are the best ones. Consequently, this work sets the scene for near-real-time irrigation decision support systems. © Wageningen Academic Publishers 2019

Note:
Related Files :
COTTON
eddy covariance
evapotranspiration
evapotranspiration
irrigation
remote sensing
Sentinel-2
Spectral modelling
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Related Content
More details
DOI :
10.3920/978-90-8686-888-9_60
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
44445
Last updated date:
02/03/2022 17:27
Creation date:
29/10/2019 13:48
Scientific Publication
Estimating cotton water requirements using sentinel-2: Model development and validation
Estimating cotton water requirements using sentinel-2: Model development and validation

Crop coefficient (Kc)-based estimation of crop water consumption is one of the most commonly used methods for irrigation management. In this study, cotton evapotranspiration was measured in the field during two seasons using the eddy covariance method. Kc was estimated as the ratio between reference evapotranspiration and the measured cotton evapotranspiration. In addition, a time series of Sentinel-2 imagery was processed to produce 21 vegetation indices (VIs) based on the sensor's unique spectral bands. The results suggest that most VIs that are based on Sentinel-2 bands are suitable predictors for cotton Kc, and that those based on the red and red-edge spectral bands are the best ones. Consequently, this work sets the scene for near-real-time irrigation decision support systems. © Wageningen Academic Publishers 2019

Scientific Publication
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